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Respiratory flow estimation from tracheal sound by adaptive filters
M Golabbakhsh1, Z Moussavi, M Aboofazeli
1Department of Electrical and Computer Engineering, University of Manitoba Winnipeg, Manitoba, Canada.
Summary
This study estimates airflow using tracheal sounds. Both parametric and nonparametric methods showed similar, accurate results, with errors around 9-11% for inspiration and expiration.
Area of Science:
- Biomedical Engineering
- Respiratory Physiology
- Signal Processing
Background:
- Accurate airflow estimation is crucial for diagnosing respiratory conditions.
- Tracheal sounds offer a non-invasive method for monitoring respiratory function.
- Parametric and nonparametric signal processing techniques can be applied to tracheal sound analysis.
Purpose of the Study:
- To evaluate the accuracy of estimating airflow using tracheal sounds.
- To compare parametric and nonparametric methods for airflow estimation.
- To determine the optimal adaptive filter order for nonparametric airflow estimation.
Main Methods:
- Utilized average power of tracheal sound (Pave) for flow estimation.
- Employed a parametric method with an exponential model for flow-Pave relationship.
- Applied adaptive filters (nonparametric method) for flow estimation, testing various filter orders.
Main Results:
- Parametric method yielded flow estimation errors of 9 ± 3% (inspiration) and 10 ± 4% (expiration).
- The third-order adaptive filter demonstrated the least error among nonparametric methods: 10 ± 3% (inspiration) and 11 ± 4% (expiration).
- Both methods provided comparable accuracy in airflow estimation.
Conclusions:
- Average tracheal sound power is a viable parameter for estimating airflow.
- Parametric and nonparametric methods offer similar accuracy for tracheal sound-based airflow estimation.
- Adaptive filters, particularly the third-order, show promise for non-invasive respiratory monitoring.
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